Overview

Dataset statistics

Number of variables22
Number of observations2938
Missing cells2563
Missing cells (%)4.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory505.1 KiB
Average record size in memory176.0 B

Variable types

Categorical2
Numeric20

Alerts

Cty has a high cardinality: 193 distinct valuesHigh cardinality
L_Expec is highly overall correlated with Dev and 12 other fieldsHigh correlation
A_Mort is highly overall correlated with L_Expec and 6 other fieldsHigh correlation
Inf_D is highly overall correlated with Mes and 4 other fieldsHigh correlation
Alc is highly overall correlated with Dev and 5 other fieldsHigh correlation
Per_Expen is highly overall correlated with Dev and 3 other fieldsHigh correlation
HepB is highly overall correlated with Polio and 1 other fieldsHigh correlation
Mes is highly overall correlated with Inf_D and 1 other fieldsHigh correlation
BMI is highly overall correlated with Dev and 9 other fieldsHigh correlation
lt5y_D is highly overall correlated with Inf_D and 4 other fieldsHigh correlation
Polio is highly overall correlated with L_Expec and 5 other fieldsHigh correlation
Dipt is highly overall correlated with L_Expec and 6 other fieldsHigh correlation
HIV is highly overall correlated with L_Expec and 1 other fieldsHigh correlation
GDP is highly overall correlated with Per_Expen and 1 other fieldsHigh correlation
Th_1-19y is highly overall correlated with Dev and 9 other fieldsHigh correlation
Th_5-9y is highly overall correlated with L_Expec and 6 other fieldsHigh correlation
Income is highly overall correlated with Dev and 10 other fieldsHigh correlation
Ed is highly overall correlated with Dev and 11 other fieldsHigh correlation
Dev is highly overall correlated with L_Expec and 7 other fieldsHigh correlation
Pop is highly overall correlated with Inf_D and 2 other fieldsHigh correlation
T_Expen is highly overall correlated with Dev and 5 other fieldsHigh correlation
Alc has 194 (6.6%) missing valuesMissing
HepB has 553 (18.8%) missing valuesMissing
BMI has 34 (1.2%) missing valuesMissing
T_Expen has 226 (7.7%) missing valuesMissing
GDP has 448 (15.2%) missing valuesMissing
Pop has 652 (22.2%) missing valuesMissing
Th_1-19y has 34 (1.2%) missing valuesMissing
Th_5-9y has 34 (1.2%) missing valuesMissing
Income has 167 (5.7%) missing valuesMissing
Ed has 163 (5.5%) missing valuesMissing
Inf_D has 848 (28.9%) zerosZeros
Per_Expen has 611 (20.8%) zerosZeros
Mes has 983 (33.5%) zerosZeros
lt5y_D has 785 (26.7%) zerosZeros
Income has 130 (4.4%) zerosZeros

Reproduction

Analysis started2023-01-07 16:47:17.442208
Analysis finished2023-01-07 16:47:56.759170
Duration39.32 seconds
Software versionpandas-profiling vdev
Download configurationconfig.json

Variables

Cty
Categorical

Distinct193
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Memory size23.1 KiB
Afghanistan
 
16
Peru
 
16
Nicaragua
 
16
Niger
 
16
Nigeria
 
16
Other values (188)
2858 

Length

Max length52
Median length34
Mean length10.041184
Min length4

Characters and Unicode

Total characters29501
Distinct characters56
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)0.3%

Sample

1st rowAfghanistan
2nd rowAfghanistan
3rd rowAfghanistan
4th rowAfghanistan
5th rowAfghanistan

Common Values

ValueCountFrequency (%)
Afghanistan 16
 
0.5%
Peru 16
 
0.5%
Nicaragua 16
 
0.5%
Niger 16
 
0.5%
Nigeria 16
 
0.5%
Norway 16
 
0.5%
Oman 16
 
0.5%
Pakistan 16
 
0.5%
Panama 16
 
0.5%
Papua New Guinea 16
 
0.5%
Other values (183) 2778
94.6%

Length

2023-01-07T11:47:56.826512image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
republic 192
 
4.5%
of 192
 
4.5%
and 97
 
2.3%
united 64
 
1.5%
democratic 48
 
1.1%
the 48
 
1.1%
guinea 48
 
1.1%
saint 33
 
0.8%
ireland 32
 
0.7%
congo 32
 
0.7%
Other values (223) 3502
81.7%

Most occurring characters

ValueCountFrequency (%)
a 4190
 
14.2%
i 2535
 
8.6%
e 2178
 
7.4%
n 2104
 
7.1%
o 1638
 
5.6%
r 1635
 
5.5%
1350
 
4.6%
u 1126
 
3.8%
l 1110
 
3.8%
t 1107
 
3.8%
Other values (46) 10528
35.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 23976
81.3%
Uppercase Letter 3967
 
13.4%
Space Separator 1350
 
4.6%
Open Punctuation 64
 
0.2%
Close Punctuation 64
 
0.2%
Other Punctuation 48
 
0.2%
Dash Punctuation 32
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 4190
17.5%
i 2535
10.6%
e 2178
 
9.1%
n 2104
 
8.8%
o 1638
 
6.8%
r 1635
 
6.8%
u 1126
 
4.7%
l 1110
 
4.6%
t 1107
 
4.6%
d 867
 
3.6%
Other values (17) 5486
22.9%
Uppercase Letter
ValueCountFrequency (%)
S 466
 
11.7%
B 336
 
8.5%
C 289
 
7.3%
M 275
 
6.9%
A 256
 
6.5%
G 240
 
6.0%
R 240
 
6.0%
T 209
 
5.3%
I 194
 
4.9%
P 193
 
4.9%
Other values (14) 1269
32.0%
Space Separator
ValueCountFrequency (%)
1350
100.0%
Open Punctuation
ValueCountFrequency (%)
( 64
100.0%
Close Punctuation
ValueCountFrequency (%)
) 64
100.0%
Other Punctuation
ValueCountFrequency (%)
' 48
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 27943
94.7%
Common 1558
 
5.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 4190
15.0%
i 2535
 
9.1%
e 2178
 
7.8%
n 2104
 
7.5%
o 1638
 
5.9%
r 1635
 
5.9%
u 1126
 
4.0%
l 1110
 
4.0%
t 1107
 
4.0%
d 867
 
3.1%
Other values (41) 9453
33.8%
Common
ValueCountFrequency (%)
1350
86.6%
( 64
 
4.1%
) 64
 
4.1%
' 48
 
3.1%
- 32
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29485
99.9%
None 16
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 4190
 
14.2%
i 2535
 
8.6%
e 2178
 
7.4%
n 2104
 
7.1%
o 1638
 
5.6%
r 1635
 
5.5%
1350
 
4.6%
u 1126
 
3.8%
l 1110
 
3.8%
t 1107
 
3.8%
Other values (45) 10512
35.7%
None
ValueCountFrequency (%)
ô 16
100.0%

Yr
Real number (ℝ)

Distinct16
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2007.5187
Minimum2000
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-07T11:47:56.917579image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile2000
Q12004
median2008
Q32012
95-th percentile2015
Maximum2015
Range15
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.6138409
Coefficient of variation (CV)0.0022982804
Kurtosis-1.2137217
Mean2007.5187
Median Absolute Deviation (MAD)4
Skewness-0.0064090274
Sum5898090
Variance21.287528
MonotonicityNot monotonic
2023-01-07T11:47:56.999541image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2013 193
 
6.6%
2015 183
 
6.2%
2014 183
 
6.2%
2012 183
 
6.2%
2011 183
 
6.2%
2010 183
 
6.2%
2009 183
 
6.2%
2008 183
 
6.2%
2007 183
 
6.2%
2006 183
 
6.2%
Other values (6) 1098
37.4%
ValueCountFrequency (%)
2000 183
6.2%
2001 183
6.2%
2002 183
6.2%
2003 183
6.2%
2004 183
6.2%
2005 183
6.2%
2006 183
6.2%
2007 183
6.2%
2008 183
6.2%
2009 183
6.2%
ValueCountFrequency (%)
2015 183
6.2%
2014 183
6.2%
2013 193
6.6%
2012 183
6.2%
2011 183
6.2%
2010 183
6.2%
2009 183
6.2%
2008 183
6.2%
2007 183
6.2%
2006 183
6.2%

Dev
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size23.1 KiB
Developing
2426 
Developed
512 

Length

Max length10
Median length10
Mean length9.8257318
Min length9

Characters and Unicode

Total characters28868
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDeveloping
2nd rowDeveloping
3rd rowDeveloping
4th rowDeveloping
5th rowDeveloping

Common Values

ValueCountFrequency (%)
Developing 2426
82.6%
Developed 512
 
17.4%

Length

2023-01-07T11:47:57.090758image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-07T11:47:57.173148image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
developing 2426
82.6%
developed 512
 
17.4%

Most occurring characters

ValueCountFrequency (%)
e 6388
22.1%
D 2938
10.2%
v 2938
10.2%
l 2938
10.2%
o 2938
10.2%
p 2938
10.2%
i 2426
 
8.4%
n 2426
 
8.4%
g 2426
 
8.4%
d 512
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 25930
89.8%
Uppercase Letter 2938
 
10.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6388
24.6%
v 2938
11.3%
l 2938
11.3%
o 2938
11.3%
p 2938
11.3%
i 2426
 
9.4%
n 2426
 
9.4%
g 2426
 
9.4%
d 512
 
2.0%
Uppercase Letter
ValueCountFrequency (%)
D 2938
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 28868
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6388
22.1%
D 2938
10.2%
v 2938
10.2%
l 2938
10.2%
o 2938
10.2%
p 2938
10.2%
i 2426
 
8.4%
n 2426
 
8.4%
g 2426
 
8.4%
d 512
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28868
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6388
22.1%
D 2938
10.2%
v 2938
10.2%
l 2938
10.2%
o 2938
10.2%
p 2938
10.2%
i 2426
 
8.4%
n 2426
 
8.4%
g 2426
 
8.4%
d 512
 
1.8%

L_Expec
Real number (ℝ)

Distinct362
Distinct (%)12.4%
Missing10
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean69.224932
Minimum36.3
Maximum89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-07T11:47:57.252369image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum36.3
5-th percentile51.4
Q163.1
median72.1
Q375.7
95-th percentile82
Maximum89
Range52.7
Interquartile range (IQR)12.6

Descriptive statistics

Standard deviation9.5238675
Coefficient of variation (CV)0.13757858
Kurtosis-0.23447739
Mean69.224932
Median Absolute Deviation (MAD)5.8
Skewness-0.63860474
Sum202690.6
Variance90.704052
MonotonicityNot monotonic
2023-01-07T11:47:57.359308image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73 45
 
1.5%
75 33
 
1.1%
78 31
 
1.1%
73.6 28
 
1.0%
73.9 25
 
0.9%
76 25
 
0.9%
81 25
 
0.9%
74.5 24
 
0.8%
74.7 24
 
0.8%
73.5 23
 
0.8%
Other values (352) 2645
90.0%
ValueCountFrequency (%)
36.3 1
< 0.1%
39 1
< 0.1%
41 1
< 0.1%
41.5 1
< 0.1%
42.3 1
< 0.1%
43.1 1
< 0.1%
43.3 1
< 0.1%
43.5 1
< 0.1%
43.8 1
< 0.1%
44 1
< 0.1%
ValueCountFrequency (%)
89 11
0.4%
88 10
0.3%
87 9
0.3%
86 15
0.5%
85 12
0.4%
84 11
0.4%
83.7 1
 
< 0.1%
83.5 2
 
0.1%
83.4 1
 
< 0.1%
83.3 1
 
< 0.1%

A_Mort
Real number (ℝ)

Distinct425
Distinct (%)14.5%
Missing10
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean164.79645
Minimum1
Maximum723
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-07T11:47:57.465051image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q174
median144
Q3228
95-th percentile398.3
Maximum723
Range722
Interquartile range (IQR)154

Descriptive statistics

Standard deviation124.29208
Coefficient of variation (CV)0.75421576
Kurtosis1.7488602
Mean164.79645
Median Absolute Deviation (MAD)76
Skewness1.1743695
Sum482524
Variance15448.521
MonotonicityNot monotonic
2023-01-07T11:47:57.563864image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 34
 
1.2%
14 30
 
1.0%
16 29
 
1.0%
11 25
 
0.9%
138 25
 
0.9%
19 23
 
0.8%
144 22
 
0.7%
15 21
 
0.7%
17 21
 
0.7%
13 21
 
0.7%
Other values (415) 2677
91.1%
ValueCountFrequency (%)
1 12
0.4%
2 8
 
0.3%
3 6
 
0.2%
4 4
 
0.1%
5 2
 
0.1%
6 13
0.4%
7 16
0.5%
8 13
0.4%
9 12
0.4%
11 25
0.9%
ValueCountFrequency (%)
723 1
< 0.1%
717 1
< 0.1%
715 1
< 0.1%
699 1
< 0.1%
693 1
< 0.1%
686 1
< 0.1%
682 1
< 0.1%
679 1
< 0.1%
675 1
< 0.1%
666 1
< 0.1%

Inf_D
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct209
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.303948
Minimum0
Maximum1800
Zeros848
Zeros (%)28.9%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-07T11:47:57.673424image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q322
95-th percentile94.15
Maximum1800
Range1800
Interquartile range (IQR)22

Descriptive statistics

Standard deviation117.9265
Coefficient of variation (CV)3.8914567
Kurtosis116.04276
Mean30.303948
Median Absolute Deviation (MAD)3
Skewness9.786963
Sum89033
Variance13906.66
MonotonicityNot monotonic
2023-01-07T11:47:57.774017image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 848
28.9%
1 342
 
11.6%
2 203
 
6.9%
3 175
 
6.0%
4 96
 
3.3%
8 57
 
1.9%
7 53
 
1.8%
9 48
 
1.6%
10 48
 
1.6%
6 46
 
1.6%
Other values (199) 1022
34.8%
ValueCountFrequency (%)
0 848
28.9%
1 342
11.6%
2 203
 
6.9%
3 175
 
6.0%
4 96
 
3.3%
5 44
 
1.5%
6 46
 
1.6%
7 53
 
1.8%
8 57
 
1.9%
9 48
 
1.6%
ValueCountFrequency (%)
1800 2
0.1%
1700 2
0.1%
1600 1
< 0.1%
1500 2
0.1%
1400 1
< 0.1%
1300 2
0.1%
1200 1
< 0.1%
1100 2
0.1%
1000 1
< 0.1%
957 1
< 0.1%

Alc
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct1076
Distinct (%)39.2%
Missing194
Missing (%)6.6%
Infinite0
Infinite (%)0.0%
Mean4.6028608
Minimum0.01
Maximum17.87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-07T11:47:57.888416image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.01
Q10.8775
median3.755
Q37.7025
95-th percentile11.96
Maximum17.87
Range17.86
Interquartile range (IQR)6.825

Descriptive statistics

Standard deviation4.0524127
Coefficient of variation (CV)0.88041174
Kurtosis-0.80290922
Mean4.6028608
Median Absolute Deviation (MAD)3.245
Skewness0.58956253
Sum12630.25
Variance16.422048
MonotonicityNot monotonic
2023-01-07T11:47:57.998475image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01 288
 
9.8%
0.03 15
 
0.5%
0.04 13
 
0.4%
0.02 12
 
0.4%
0.09 12
 
0.4%
0.21 10
 
0.3%
0.06 10
 
0.3%
1.18 10
 
0.3%
0.05 9
 
0.3%
0.49 9
 
0.3%
Other values (1066) 2356
80.2%
(Missing) 194
 
6.6%
ValueCountFrequency (%)
0.01 288
9.8%
0.02 12
 
0.4%
0.03 15
 
0.5%
0.04 13
 
0.4%
0.05 9
 
0.3%
0.06 10
 
0.3%
0.07 4
 
0.1%
0.08 9
 
0.3%
0.09 12
 
0.4%
0.1 7
 
0.2%
ValueCountFrequency (%)
17.87 1
< 0.1%
17.31 1
< 0.1%
16.99 1
< 0.1%
16.58 1
< 0.1%
16.35 1
< 0.1%
15.52 1
< 0.1%
15.19 1
< 0.1%
15.14 1
< 0.1%
15.07 1
< 0.1%
15.04 2
0.1%

Per_Expen
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct2328
Distinct (%)79.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean738.2513
Minimum0
Maximum19479.912
Zeros611
Zeros (%)20.8%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-07T11:47:58.119884image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.6853426
median64.912906
Q3441.53414
95-th percentile4506.6385
Maximum19479.912
Range19479.912
Interquartile range (IQR)436.8488

Descriptive statistics

Standard deviation1987.9149
Coefficient of variation (CV)2.6927347
Kurtosis26.573387
Mean738.2513
Median Absolute Deviation (MAD)64.912906
Skewness4.6520513
Sum2168982.3
Variance3951805.5
MonotonicityNot monotonic
2023-01-07T11:47:58.238011image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 611
 
20.8%
71.27962362 1
 
< 0.1%
3.304039899 1
 
< 0.1%
218.5716179 1
 
< 0.1%
36.81621175 1
 
< 0.1%
2.542436908 1
 
< 0.1%
2.092343893 1
 
< 0.1%
22.35595448 1
 
< 0.1%
15.25518816 1
 
< 0.1%
31.50243237 1
 
< 0.1%
Other values (2318) 2318
78.9%
ValueCountFrequency (%)
0 611
20.8%
0.09987219 1
 
< 0.1%
0.108055973 1
 
< 0.1%
0.27564826 1
 
< 0.1%
0.328418056 1
 
< 0.1%
0.358651421 1
 
< 0.1%
0.388253772 1
 
< 0.1%
0.397228764 1
 
< 0.1%
0.442802404 1
 
< 0.1%
0.5305728 1
 
< 0.1%
ValueCountFrequency (%)
19479.91161 1
< 0.1%
19099.04506 1
< 0.1%
18961.3486 1
< 0.1%
18822.86732 1
< 0.1%
18379.32974 1
< 0.1%
17028.52798 1
< 0.1%
16255.16198 1
< 0.1%
15515.75234 1
< 0.1%
15345.4907 1
< 0.1%
15268.06445 1
< 0.1%

HepB
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct87
Distinct (%)3.6%
Missing553
Missing (%)18.8%
Infinite0
Infinite (%)0.0%
Mean80.940461
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-07T11:47:58.359353image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q177
median92
Q397
95-th percentile99
Maximum99
Range98
Interquartile range (IQR)20

Descriptive statistics

Standard deviation25.070016
Coefficient of variation (CV)0.30973403
Kurtosis2.7702594
Mean80.940461
Median Absolute Deviation (MAD)6
Skewness-1.9308451
Sum193043
Variance628.50568
MonotonicityNot monotonic
2023-01-07T11:47:58.469816image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 240
 
8.2%
98 210
 
7.1%
96 167
 
5.7%
97 155
 
5.3%
95 149
 
5.1%
94 127
 
4.3%
93 101
 
3.4%
92 92
 
3.1%
91 75
 
2.6%
89 71
 
2.4%
Other values (77) 998
34.0%
(Missing) 553
18.8%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 4
 
0.1%
4 4
 
0.1%
5 9
 
0.3%
6 17
 
0.6%
7 20
 
0.7%
8 39
1.3%
9 65
2.2%
11 1
 
< 0.1%
12 1
 
< 0.1%
ValueCountFrequency (%)
99 240
8.2%
98 210
7.1%
97 155
5.3%
96 167
5.7%
95 149
5.1%
94 127
4.3%
93 101
3.4%
92 92
 
3.1%
91 75
 
2.6%
89 71
 
2.4%

Mes
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct958
Distinct (%)32.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2419.5922
Minimum0
Maximum212183
Zeros983
Zeros (%)33.5%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-07T11:47:58.580088image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median17
Q3360.25
95-th percentile9985.55
Maximum212183
Range212183
Interquartile range (IQR)360.25

Descriptive statistics

Standard deviation11467.272
Coefficient of variation (CV)4.7393409
Kurtosis114.8599
Mean2419.5922
Median Absolute Deviation (MAD)17
Skewness9.4413319
Sum7108762
Variance1.3149834 × 108
MonotonicityNot monotonic
2023-01-07T11:47:58.686830image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 983
33.5%
1 104
 
3.5%
2 68
 
2.3%
3 44
 
1.5%
4 33
 
1.1%
6 29
 
1.0%
7 28
 
1.0%
5 25
 
0.9%
8 24
 
0.8%
9 22
 
0.7%
Other values (948) 1578
53.7%
ValueCountFrequency (%)
0 983
33.5%
1 104
 
3.5%
2 68
 
2.3%
3 44
 
1.5%
4 33
 
1.1%
5 25
 
0.9%
6 29
 
1.0%
7 28
 
1.0%
8 24
 
0.8%
9 22
 
0.7%
ValueCountFrequency (%)
212183 1
< 0.1%
182485 1
< 0.1%
168107 1
< 0.1%
141258 1
< 0.1%
133802 1
< 0.1%
131441 1
< 0.1%
124219 1
< 0.1%
118712 1
< 0.1%
110927 1
< 0.1%
109023 1
< 0.1%

BMI
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct608
Distinct (%)20.9%
Missing34
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean38.321247
Minimum1
Maximum87.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-07T11:47:58.794843image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.2
Q119.3
median43.5
Q356.2
95-th percentile64.785
Maximum87.3
Range86.3
Interquartile range (IQR)36.9

Descriptive statistics

Standard deviation20.044034
Coefficient of variation (CV)0.52305275
Kurtosis-1.2910955
Mean38.321247
Median Absolute Deviation (MAD)16.3
Skewness-0.2193116
Sum111284.9
Variance401.76328
MonotonicityNot monotonic
2023-01-07T11:47:58.897652image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
58.5 18
 
0.6%
55.8 16
 
0.5%
57 16
 
0.5%
54.2 15
 
0.5%
59.9 15
 
0.5%
59.3 14
 
0.5%
52.8 13
 
0.4%
55 13
 
0.4%
59.4 13
 
0.4%
56.5 13
 
0.4%
Other values (598) 2758
93.9%
(Missing) 34
 
1.2%
ValueCountFrequency (%)
1 1
 
< 0.1%
1.4 2
 
0.1%
1.8 1
 
< 0.1%
1.9 1
 
< 0.1%
2 1
 
< 0.1%
2.1 11
0.4%
2.2 9
0.3%
2.3 6
0.2%
2.4 5
0.2%
2.5 8
0.3%
ValueCountFrequency (%)
87.3 1
< 0.1%
83.3 1
< 0.1%
82.8 1
< 0.1%
81.6 1
< 0.1%
79.3 1
< 0.1%
77.6 1
< 0.1%
77.3 1
< 0.1%
77.1 1
< 0.1%
76.7 1
< 0.1%
76.2 1
< 0.1%

lt5y_D
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct252
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.035739
Minimum0
Maximum2500
Zeros785
Zeros (%)26.7%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-07T11:47:59.002205image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q328
95-th percentile138
Maximum2500
Range2500
Interquartile range (IQR)28

Descriptive statistics

Standard deviation160.44555
Coefficient of variation (CV)3.8168842
Kurtosis109.7528
Mean42.035739
Median Absolute Deviation (MAD)4
Skewness9.4950647
Sum123501
Variance25742.774
MonotonicityNot monotonic
2023-01-07T11:47:59.107064image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 785
26.7%
1 361
 
12.3%
2 163
 
5.5%
4 161
 
5.5%
3 129
 
4.4%
12 53
 
1.8%
8 49
 
1.7%
6 48
 
1.6%
10 47
 
1.6%
5 44
 
1.5%
Other values (242) 1098
37.4%
ValueCountFrequency (%)
0 785
26.7%
1 361
12.3%
2 163
 
5.5%
3 129
 
4.4%
4 161
 
5.5%
5 44
 
1.5%
6 48
 
1.6%
7 30
 
1.0%
8 49
 
1.7%
9 40
 
1.4%
ValueCountFrequency (%)
2500 1
< 0.1%
2400 1
< 0.1%
2300 1
< 0.1%
2200 1
< 0.1%
2100 1
< 0.1%
2000 2
0.1%
1900 1
< 0.1%
1800 1
< 0.1%
1700 1
< 0.1%
1600 1
< 0.1%

Polio
Real number (ℝ)

Distinct73
Distinct (%)2.5%
Missing19
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean82.550188
Minimum3
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-07T11:47:59.214256image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile9
Q178
median93
Q397
95-th percentile99
Maximum99
Range96
Interquartile range (IQR)19

Descriptive statistics

Standard deviation23.428046
Coefficient of variation (CV)0.28380366
Kurtosis3.7765098
Mean82.550188
Median Absolute Deviation (MAD)6
Skewness-2.0980532
Sum240964
Variance548.87334
MonotonicityNot monotonic
2023-01-07T11:47:59.328790image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 376
 
12.8%
98 255
 
8.7%
96 207
 
7.0%
97 205
 
7.0%
95 180
 
6.1%
94 159
 
5.4%
93 120
 
4.1%
92 96
 
3.3%
91 88
 
3.0%
9 71
 
2.4%
Other values (63) 1162
39.6%
ValueCountFrequency (%)
3 7
 
0.2%
4 11
 
0.4%
5 8
 
0.3%
6 11
 
0.4%
7 24
 
0.8%
8 40
1.4%
9 71
2.4%
17 1
 
< 0.1%
23 1
 
< 0.1%
24 2
 
0.1%
ValueCountFrequency (%)
99 376
12.8%
98 255
8.7%
97 205
7.0%
96 207
7.0%
95 180
6.1%
94 159
5.4%
93 120
 
4.1%
92 96
 
3.3%
91 88
 
3.0%
89 56
 
1.9%

T_Expen
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct818
Distinct (%)30.2%
Missing226
Missing (%)7.7%
Infinite0
Infinite (%)0.0%
Mean5.9381895
Minimum0.37
Maximum17.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-07T11:47:59.444027image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.37
5-th percentile1.93
Q14.26
median5.755
Q37.4925
95-th percentile9.76
Maximum17.6
Range17.23
Interquartile range (IQR)3.2325

Descriptive statistics

Standard deviation2.4983197
Coefficient of variation (CV)0.42072077
Kurtosis1.1562705
Mean5.9381895
Median Absolute Deviation (MAD)1.59
Skewness0.61868555
Sum16104.37
Variance6.2416012
MonotonicityNot monotonic
2023-01-07T11:47:59.557235image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.6 15
 
0.5%
6.7 12
 
0.4%
5.6 11
 
0.4%
9.1 10
 
0.3%
5.64 10
 
0.3%
5.9 10
 
0.3%
5.3 10
 
0.3%
5.25 10
 
0.3%
3.4 10
 
0.3%
4.2 9
 
0.3%
Other values (808) 2605
88.7%
(Missing) 226
 
7.7%
ValueCountFrequency (%)
0.37 1
 
< 0.1%
0.65 1
 
< 0.1%
0.74 1
 
< 0.1%
0.76 1
 
< 0.1%
0.92 1
 
< 0.1%
1.1 2
0.1%
1.12 3
0.1%
1.15 2
0.1%
1.17 2
0.1%
1.18 3
0.1%
ValueCountFrequency (%)
17.6 1
< 0.1%
17.24 1
< 0.1%
17.2 2
0.1%
17.14 1
< 0.1%
17 1
< 0.1%
16.9 1
< 0.1%
16.61 1
< 0.1%
16.2 1
< 0.1%
15.6 1
< 0.1%
15.57 1
< 0.1%

Dipt
Real number (ℝ)

Distinct81
Distinct (%)2.8%
Missing19
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean82.324084
Minimum2
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-07T11:47:59.977995image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile9
Q178
median93
Q397
95-th percentile99
Maximum99
Range97
Interquartile range (IQR)19

Descriptive statistics

Standard deviation23.716912
Coefficient of variation (CV)0.28809203
Kurtosis3.558143
Mean82.324084
Median Absolute Deviation (MAD)6
Skewness-2.0727529
Sum240304
Variance562.49192
MonotonicityNot monotonic
2023-01-07T11:48:00.091647image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 350
 
11.9%
98 254
 
8.6%
97 205
 
7.0%
96 201
 
6.8%
95 200
 
6.8%
94 149
 
5.1%
93 120
 
4.1%
92 100
 
3.4%
91 91
 
3.1%
89 76
 
2.6%
Other values (71) 1173
39.9%
ValueCountFrequency (%)
2 1
 
< 0.1%
3 4
 
0.1%
4 12
 
0.4%
5 10
 
0.3%
6 16
 
0.5%
7 21
 
0.7%
8 39
1.3%
9 67
2.3%
16 1
 
< 0.1%
19 1
 
< 0.1%
ValueCountFrequency (%)
99 350
11.9%
98 254
8.6%
97 205
7.0%
96 201
6.8%
95 200
6.8%
94 149
5.1%
93 120
 
4.1%
92 100
 
3.4%
91 91
 
3.1%
89 76
 
2.6%

HIV
Real number (ℝ)

Distinct200
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7421035
Minimum0.1
Maximum50.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-07T11:48:00.202607image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.1
Q10.1
median0.1
Q30.8
95-th percentile8.515
Maximum50.6
Range50.5
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation5.0777845
Coefficient of variation (CV)2.9147434
Kurtosis34.892008
Mean1.7421035
Median Absolute Deviation (MAD)0
Skewness5.396112
Sum5118.3
Variance25.783896
MonotonicityNot monotonic
2023-01-07T11:48:00.314338image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1 1781
60.6%
0.2 124
 
4.2%
0.3 115
 
3.9%
0.4 69
 
2.3%
0.5 42
 
1.4%
0.6 35
 
1.2%
0.9 32
 
1.1%
0.8 32
 
1.1%
0.7 29
 
1.0%
1.5 21
 
0.7%
Other values (190) 658
 
22.4%
ValueCountFrequency (%)
0.1 1781
60.6%
0.2 124
 
4.2%
0.3 115
 
3.9%
0.4 69
 
2.3%
0.5 42
 
1.4%
0.6 35
 
1.2%
0.7 29
 
1.0%
0.8 32
 
1.1%
0.9 32
 
1.1%
1 12
 
0.4%
ValueCountFrequency (%)
50.6 1
< 0.1%
50.3 1
< 0.1%
49.9 1
< 0.1%
49.1 1
< 0.1%
48.8 1
< 0.1%
46.4 1
< 0.1%
43.7 1
< 0.1%
43.5 1
< 0.1%
42.1 1
< 0.1%
40.7 1
< 0.1%

GDP
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct2490
Distinct (%)100.0%
Missing448
Missing (%)15.2%
Infinite0
Infinite (%)0.0%
Mean7483.1585
Minimum1.68135
Maximum119172.74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-07T11:48:00.423511image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1.68135
5-th percentile68.050015
Q1463.93563
median1766.9476
Q35910.8063
95-th percentile41606.848
Maximum119172.74
Range119171.06
Interquartile range (IQR)5446.8707

Descriptive statistics

Standard deviation14270.169
Coefficient of variation (CV)1.9069714
Kurtosis12.333074
Mean7483.1585
Median Absolute Deviation (MAD)1592.4561
Skewness3.2066549
Sum18633065
Variance2.0363773 × 108
MonotonicityNot monotonic
2023-01-07T11:48:00.541584image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
584.25921 1
 
< 0.1%
354.8185998 1
 
< 0.1%
358.99731 1
 
< 0.1%
43.646498 1
 
< 0.1%
416.14838 1
 
< 0.1%
391.515524 1
 
< 0.1%
375.5819866 1
 
< 0.1%
348.151511 1
 
< 0.1%
341.2894618 1
 
< 0.1%
292.55962 1
 
< 0.1%
Other values (2480) 2480
84.4%
(Missing) 448
 
15.2%
ValueCountFrequency (%)
1.68135 1
< 0.1%
3.685949 1
< 0.1%
4.6135745 1
< 0.1%
5.6687264 1
< 0.1%
8.376432 1
< 0.1%
11.147277 1
< 0.1%
11.33678 1
< 0.1%
11.553196 1
< 0.1%
11.631377 1
< 0.1%
12.1789279 1
< 0.1%
ValueCountFrequency (%)
119172.7418 1
< 0.1%
115761.577 1
< 0.1%
114293.8433 1
< 0.1%
113751.85 1
< 0.1%
89739.7117 1
< 0.1%
88564.82298 1
< 0.1%
87998.44468 1
< 0.1%
87646.75346 1
< 0.1%
86852.7119 1
< 0.1%
85948.746 1
< 0.1%

Pop
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct2278
Distinct (%)99.7%
Missing652
Missing (%)22.2%
Infinite0
Infinite (%)0.0%
Mean12753375
Minimum34
Maximum1.2938593 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-07T11:48:00.658899image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum34
5-th percentile9617.5
Q1195793.25
median1386542
Q37420359
95-th percentile47554416
Maximum1.2938593 × 109
Range1.2938593 × 109
Interquartile range (IQR)7224565.8

Descriptive statistics

Standard deviation61012097
Coefficient of variation (CV)4.7839961
Kurtosis298.01027
Mean12753375
Median Absolute Deviation (MAD)1357309.5
Skewness15.916236
Sum2.9154216 × 1010
Variance3.7224759 × 1015
MonotonicityNot monotonic
2023-01-07T11:48:00.781292image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
444 4
 
0.1%
718239 2
 
0.1%
1141 2
 
0.1%
26868 2
 
0.1%
127445 2
 
0.1%
292 2
 
0.1%
51448196 1
 
< 0.1%
12262 1
 
< 0.1%
15228525 1
 
< 0.1%
14668338 1
 
< 0.1%
Other values (2268) 2268
77.2%
(Missing) 652
 
22.2%
ValueCountFrequency (%)
34 1
< 0.1%
36 1
< 0.1%
41 1
< 0.1%
43 1
< 0.1%
123 1
< 0.1%
135 1
< 0.1%
146 1
< 0.1%
286 1
< 0.1%
292 2
0.1%
297 1
< 0.1%
ValueCountFrequency (%)
1293859294 1
< 0.1%
1179681239 1
< 0.1%
1161977719 1
< 0.1%
1144118674 1
< 0.1%
1126135777 1
< 0.1%
258162113 1
< 0.1%
255131116 1
< 0.1%
248883232 1
< 0.1%
242524123 1
< 0.1%
236159276 1
< 0.1%

Th_1-19y
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct200
Distinct (%)6.9%
Missing34
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean4.8397039
Minimum0.1
Maximum27.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-07T11:48:00.902817image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.6
Q11.6
median3.3
Q37.2
95-th percentile13.8
Maximum27.7
Range27.6
Interquartile range (IQR)5.6

Descriptive statistics

Standard deviation4.4201949
Coefficient of variation (CV)0.9133193
Kurtosis3.9704387
Mean4.8397039
Median Absolute Deviation (MAD)2.3
Skewness1.7114711
Sum14054.5
Variance19.538123
MonotonicityNot monotonic
2023-01-07T11:48:01.011290image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 74
 
2.5%
1.9 65
 
2.2%
0.8 64
 
2.2%
0.7 63
 
2.1%
1.2 62
 
2.1%
2.1 61
 
2.1%
1.5 60
 
2.0%
2.2 58
 
2.0%
0.9 57
 
1.9%
2 57
 
1.9%
Other values (190) 2283
77.7%
ValueCountFrequency (%)
0.1 28
 
1.0%
0.2 40
1.4%
0.3 32
1.1%
0.4 5
 
0.2%
0.5 35
1.2%
0.6 41
1.4%
0.7 63
2.1%
0.8 64
2.2%
0.9 57
1.9%
1 74
2.5%
ValueCountFrequency (%)
27.7 1
 
< 0.1%
27.5 1
 
< 0.1%
27.4 1
 
< 0.1%
27.3 1
 
< 0.1%
27.2 2
0.1%
27.1 2
0.1%
27 3
0.1%
26.9 2
0.1%
26.8 2
0.1%
26.7 1
 
< 0.1%

Th_5-9y
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct207
Distinct (%)7.1%
Missing34
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean4.8703168
Minimum0.1
Maximum28.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-07T11:48:01.128729image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.5
Q11.5
median3.3
Q37.2
95-th percentile13.8
Maximum28.6
Range28.5
Interquartile range (IQR)5.7

Descriptive statistics

Standard deviation4.5088821
Coefficient of variation (CV)0.92578825
Kurtosis4.3587303
Mean4.8703168
Median Absolute Deviation (MAD)2.3
Skewness1.777424
Sum14143.4
Variance20.330018
MonotonicityNot monotonic
2023-01-07T11:48:01.239911image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9 69
 
2.3%
1.1 67
 
2.3%
0.5 63
 
2.1%
1.9 63
 
2.1%
1 62
 
2.1%
2.1 61
 
2.1%
1.3 59
 
2.0%
1.5 57
 
1.9%
1.7 55
 
1.9%
0.6 54
 
1.8%
Other values (197) 2294
78.1%
ValueCountFrequency (%)
0.1 37
1.3%
0.2 45
1.5%
0.3 25
 
0.9%
0.4 17
 
0.6%
0.5 63
2.1%
0.6 54
1.8%
0.7 46
1.6%
0.8 36
1.2%
0.9 69
2.3%
1 62
2.1%
ValueCountFrequency (%)
28.6 1
< 0.1%
28.5 1
< 0.1%
28.4 1
< 0.1%
28.3 1
< 0.1%
28.2 1
< 0.1%
28.1 1
< 0.1%
28 2
0.1%
27.9 1
< 0.1%
27.8 2
0.1%
27.7 1
< 0.1%

Income
Real number (ℝ)

HIGH CORRELATION
MISSING
ZEROS

Distinct625
Distinct (%)22.6%
Missing167
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean0.62755106
Minimum0
Maximum0.948
Zeros130
Zeros (%)4.4%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-07T11:48:01.355124image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.277
Q10.493
median0.677
Q30.779
95-th percentile0.892
Maximum0.948
Range0.948
Interquartile range (IQR)0.286

Descriptive statistics

Standard deviation0.21090356
Coefficient of variation (CV)0.33607393
Kurtosis1.3928142
Mean0.62755106
Median Absolute Deviation (MAD)0.127
Skewness-1.1437627
Sum1738.944
Variance0.04448031
MonotonicityNot monotonic
2023-01-07T11:48:01.463486image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 130
 
4.4%
0.7 17
 
0.6%
0.739 13
 
0.4%
0.714 12
 
0.4%
0.636 12
 
0.4%
0.737 11
 
0.4%
0.734 11
 
0.4%
0.797 11
 
0.4%
0.86 11
 
0.4%
0.703 11
 
0.4%
Other values (615) 2532
86.2%
(Missing) 167
 
5.7%
ValueCountFrequency (%)
0 130
4.4%
0.253 1
 
< 0.1%
0.255 1
 
< 0.1%
0.261 1
 
< 0.1%
0.266 1
 
< 0.1%
0.268 3
 
0.1%
0.27 1
 
< 0.1%
0.276 1
 
< 0.1%
0.278 1
 
< 0.1%
0.279 1
 
< 0.1%
ValueCountFrequency (%)
0.948 1
 
< 0.1%
0.945 1
 
< 0.1%
0.942 1
 
< 0.1%
0.941 1
 
< 0.1%
0.939 1
 
< 0.1%
0.938 1
 
< 0.1%
0.937 1
 
< 0.1%
0.936 5
0.2%
0.934 2
 
0.1%
0.933 1
 
< 0.1%

Ed
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct173
Distinct (%)6.2%
Missing163
Missing (%)5.5%
Infinite0
Infinite (%)0.0%
Mean11.992793
Minimum0
Maximum20.7
Zeros28
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-07T11:48:01.574139image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.8
Q110.1
median12.3
Q314.3
95-th percentile16.8
Maximum20.7
Range20.7
Interquartile range (IQR)4.2

Descriptive statistics

Standard deviation3.3589197
Coefficient of variation (CV)0.28007819
Kurtosis0.88615127
Mean11.992793
Median Absolute Deviation (MAD)2.1
Skewness-0.60243654
Sum33280
Variance11.282342
MonotonicityNot monotonic
2023-01-07T11:48:01.677741image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.9 58
 
2.0%
13.3 52
 
1.8%
12.5 49
 
1.7%
12.8 46
 
1.6%
12.3 44
 
1.5%
12.6 43
 
1.5%
12.4 42
 
1.4%
10.7 41
 
1.4%
11.9 41
 
1.4%
12.7 40
 
1.4%
Other values (163) 2319
78.9%
(Missing) 163
 
5.5%
ValueCountFrequency (%)
0 28
1.0%
2.8 1
 
< 0.1%
2.9 4
 
0.1%
3 1
 
< 0.1%
3.1 1
 
< 0.1%
3.3 1
 
< 0.1%
3.4 1
 
< 0.1%
3.5 3
 
0.1%
3.6 1
 
< 0.1%
3.7 2
 
0.1%
ValueCountFrequency (%)
20.7 1
 
< 0.1%
20.6 1
 
< 0.1%
20.5 1
 
< 0.1%
20.4 3
0.1%
20.3 4
0.1%
20.1 2
0.1%
19.8 1
 
< 0.1%
19.7 1
 
< 0.1%
19.5 3
0.1%
19.3 2
0.1%

Interactions

2023-01-07T11:47:54.403038image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:19.851841image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:21.780810image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:23.582619image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:25.520514image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:27.455047image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:29.238121image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:31.076760image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:32.929650image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:34.622050image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:36.319784image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:38.199058image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:39.916502image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:41.699256image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:43.620288image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:45.348022image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:47.130759image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:48.915081image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:50.645266image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:52.665050image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:54.496011image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:19.961203image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:21.874242image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:23.768303image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:25.614608image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:27.549845image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:29.336889image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:31.298622image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:33.017538image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:34.710904image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:36.567168image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:38.290543image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:40.012038image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:41.791130image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:43.710826image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:45.441321image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:47.226936image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:49.010545image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:50.978842image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:52.757841image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:54.580403image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:20.052333image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:21.959761image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:23.859383image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:25.702250image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:27.636741image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:29.428353image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:31.382575image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:33.101511image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:34.803753image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:36.653016image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:38.374489image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:40.100975image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:41.874325image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:43.796636image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:45.529480image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:47.313291image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:49.096285image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:51.064888image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:52.850112image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:54.671138image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:20.148705image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:22.053690image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:23.957673image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:25.795542image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:27.730642image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:29.523628image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:31.475535image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:33.193157image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:34.901718image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:36.746240image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:38.463194image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:40.194110image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:41.963084image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:43.887002image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:45.622849image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:47.406043image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:49.186648image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:51.160416image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:52.945500image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:54.761257image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:20.245252image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:22.146219image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:24.058615image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:25.888688image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:27.824737image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:29.619597image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:31.571193image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:33.284337image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:34.991212image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:36.837921image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:38.552923image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:40.287343image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:42.052455image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:43.978729image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:45.717179image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:47.500166image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:49.275738image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:51.256781image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:53.043177image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:54.851178image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:20.343041image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:22.239906image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:24.155516image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:25.982257image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:27.914764image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:29.716143image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:31.663285image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:33.372952image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:35.078848image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:36.933012image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:38.643295image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:40.379503image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:42.144752image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:44.069264image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:45.810178image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:47.595284image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:49.366687image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:51.352901image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:53.135322image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:54.944988image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:20.443604image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:22.340636image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:24.256202image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:26.081076image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:28.013256image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:29.815430image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:31.754283image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:33.464936image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:35.171438image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:37.030856image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:38.736898image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:40.477021image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:42.240909image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:44.165234image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-01-07T11:47:45.015447image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:46.781428image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:48.569119image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:50.308764image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:52.319337image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:54.077200image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:55.846440image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:21.511388image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:23.316475image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:25.260897image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:27.192182image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:28.980271image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:30.811264image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:32.686988image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:34.374758image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:36.076989image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:37.951359image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:39.670860image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:41.438340image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:43.373138image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:45.099591image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:46.869435image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:48.656171image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:50.393616image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:52.406864image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:54.159455image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:55.929262image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:21.606511image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:23.408096image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:25.352107image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:27.284954image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:29.070741image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:30.904760image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:32.770662image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:34.463542image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:36.164199image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:38.038283image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:39.757544image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:41.528316image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:43.462121image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:45.187612image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:46.963192image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:48.747709image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:50.482129image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:52.496534image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:54.247065image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:56.008978image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:21.694900image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:23.491833image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:25.436518image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:27.371950image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:29.155070image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:30.991652image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:32.850310image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:34.543392image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:36.242530image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:38.117953image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:39.837242image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:41.615183image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:43.541095image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:45.268393image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:47.047510image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:48.830623image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:50.564901image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:52.582166image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-07T11:47:54.324777image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-01-07T11:48:01.784629image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2023-01-07T11:48:01.959106image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-01-07T11:48:02.137681image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-01-07T11:48:02.324899image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-01-07T11:48:02.510397image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2023-01-07T11:47:56.149354image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-07T11:47:56.408542image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-01-07T11:47:56.613432image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CtyYrDevL_ExpecA_MortInf_DAlcPer_ExpenHepBMesBMIlt5y_DPolioT_ExpenDiptHIVGDPPopTh_1-19yTh_5-9yIncomeEd
0Afghanistan2015Developing65.0263.0620.0171.27962465.0115419.1836.08.1665.00.1584.25921033736494.017.217.30.47910.1
1Afghanistan2014Developing59.9271.0640.0173.52358262.049218.68658.08.1862.00.1612.696514327582.017.517.50.47610.0
2Afghanistan2013Developing59.9268.0660.0173.21924364.043018.18962.08.1364.00.1631.74497631731688.017.717.70.4709.9
3Afghanistan2012Developing59.5272.0690.0178.18421567.0278717.69367.08.5267.00.1669.9590003696958.017.918.00.4639.8
4Afghanistan2011Developing59.2275.0710.017.09710968.0301317.29768.07.8768.00.163.5372312978599.018.218.20.4549.5
5Afghanistan2010Developing58.8279.0740.0179.67936766.0198916.710266.09.2066.00.1553.3289402883167.018.418.40.4489.2
6Afghanistan2009Developing58.6281.0770.0156.76221763.0286116.210663.09.4263.00.1445.893298284331.018.618.70.4348.9
7Afghanistan2008Developing58.1287.0800.0325.87392564.0159915.711064.08.3364.00.1373.3611162729431.018.818.90.4338.7
8Afghanistan2007Developing57.5295.0820.0210.91015663.0114115.211363.06.7363.00.1369.83579626616792.019.019.10.4158.4
9Afghanistan2006Developing57.3295.0840.0317.17151864.0199014.711658.07.4358.00.1272.5637702589345.019.219.30.4058.1
CtyYrDevL_ExpecA_MortInf_DAlcPer_ExpenHepBMesBMIlt5y_DPolioT_ExpenDiptHIVGDPPopTh_1-19yTh_5-9yIncomeEd
2928Zimbabwe2009Developing50.0587.0304.641.04002173.085329.04569.06.2673.018.165.8241211381599.07.57.40.4199.9
2929Zimbabwe2008Developing48.2632.0303.5620.84342975.0028.64675.04.9675.020.5325.67857313558469.07.87.80.4219.7
2930Zimbabwe2007Developing46.667.0293.8829.81456672.024228.24673.04.4773.023.7396.9982171332999.08.28.20.4149.6
2931Zimbabwe2006Developing45.47.0284.5734.26216968.021227.94571.05.127.026.8414.79623213124267.08.68.60.4089.5
2932Zimbabwe2005Developing44.6717.0284.148.71740965.042027.54369.06.4468.030.3444.765750129432.09.09.00.4069.3
2933Zimbabwe2004Developing44.3723.0274.360.00000068.03127.14267.07.1365.033.6454.36665412777511.09.49.40.4079.2
2934Zimbabwe2003Developing44.5715.0264.060.0000007.099826.7417.06.5268.036.7453.35115512633897.09.89.90.4189.5
2935Zimbabwe2002Developing44.873.0254.430.00000073.030426.34073.06.5371.039.857.348340125525.01.21.30.42710.0
2936Zimbabwe2001Developing45.3686.0251.720.00000076.052925.93976.06.1675.042.1548.58731212366165.01.61.70.4279.8
2937Zimbabwe2000Developing46.0665.0241.680.00000079.0148325.53978.07.1078.043.5547.35887812222251.011.011.20.4349.8